Universal domain adaptation (UniDA) is a practical but challenging problem, in which
information about the relation between the source and the target domains is not given for …

Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-
labeled source domain to a related but unlabeled target domain. While the source model is …

Universal domain adaptation (UniDA) is the unsupervised domain adaptation with label
shift. UniDA aims to classify unlabeled target samples into one of the" known" categories or …

In many real-world applications, test data may commonly exhibit categorical shifts,
characterized by the emergence of novel classes, as well as distribution shifts arising from …

Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source
domain to an unlabeled target domain without assuming how much the label-sets of the two …